setwd("/Users/chengg/Google Drive/EPICON/Mycobiome/Fungal ITS/statistic/Total.fungi")
library(vegan)
library(betapart)
library(colorRamps)
source("weighted Fst function.r")
rm(list = ls())
load("EPICON.data.preparation.RC.bNTI.ted.2019.04.19.Rdata")
mean(Lagg[Lagg$TP<8 & Lagg$Habitat=="Leaf" & Lagg$Treatment=="Control",]$jtudisper)
## [1] 0.4417626
mean(Lagg[Lagg$TP>8 & Lagg$Habitat=="Leaf" & Lagg$Treatment=="Control",]$jtudisper)
## [1] 0.3218303
# bray
fung1<-fung[env$Treatment1=="Control"& env$TP>0,]
env1<-env[env$Treatment1=="Control"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Control" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Control" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[1:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[1:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 1.12929 0.070581 12.542 3.544e-16 ***
## Residuals 85 0.47834 0.005628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.2136 5.2136 43.692 0.30407 0.001 ***
## Residuals 100 11.9326 0.1193 0.69593
## Total 101 17.1462 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.18236 0.0113977 7.2263 3.122e-10 ***
## Residuals 85 0.13407 0.0015773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.7586 3.7586 37.717 0.27387 0.001 ***
## Residuals 100 9.9654 0.0997 0.72613
## Total 101 13.7240 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.017296 0.00108099 1.1683 0.3099
## Residuals 85 0.078645 0.00092523
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.2329 5.2329 48.077 0.32468 0.001 ***
## Residuals 100 10.8845 0.1088 0.67532
## Total 101 16.1174 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.049327 0.0030829 2.4884 0.003714 **
## Residuals 85 0.105308 0.0012389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.3691 1.36908 12.583 0.11176 0.001 ***
## Residuals 100 10.8806 0.10881 0.88824
## Total 101 12.2497 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# jaccard
fung1<-fung[env$Treatment1=="Control"& env$TP>0,]
env1<-env[env$Treatment1=="Control"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Control" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Control" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"jaccard")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[1:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[1:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 1.13877 0.071173 16.885 < 2.2e-16 ***
## Residuals 85 0.35828 0.004215
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.9582 5.9582 30.634 0.2345 0.001 ***
## Residuals 100 19.4495 0.1945 0.7655
## Total 101 25.4077 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.19654 0.012284 7.6017 1.027e-10 ***
## Residuals 85 0.13736 0.001616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 4.0316 4.0316 21.589 0.17756 0.001 ***
## Residuals 100 18.6744 0.1867 0.82244
## Total 101 22.7060 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.017209 0.00107557 1.1708 0.3079
## Residuals 85 0.078086 0.00091866
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.3773 5.3773 26.716 0.21083 0.001 ***
## Residuals 100 20.1280 0.2013 0.78917
## Total 101 25.5054 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.042984 0.0026865 2.5662 0.002783 **
## Residuals 85 0.088985 0.0010469
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.6828 1.68284 8.387 0.07738 0.001 ***
## Residuals 100 20.0648 0.20065 0.92262
## Total 101 21.7477 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# turnover
fung1<-fung[env$Treatment1=="Control"& env$TP>0,]
env1<-env[env$Treatment1=="Control"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Control" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Control" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[1:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[1:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.52992 0.033120 7.5913 1.058e-10 ***
## Residuals 85 0.37084 0.004363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 4.027 4.027 22.494 0.18363 0.001 ***
## Residuals 100 17.903 0.179 0.81637
## Total 101 21.930 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.058778 0.0036736 1.2751 0.2321
## Residuals 85 0.244884 0.0028810
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.7309 3.7309 23.682 0.19147 0.001 ***
## Residuals 100 15.7543 0.1575 0.80853
## Total 101 19.4852 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.038772 0.0024232 1.053 0.4122
## Residuals 85 0.195617 0.0023014
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.7137 3.7137 21.666 0.17808 0.001 ***
## Residuals 100 17.1407 0.1714 0.82192
## Total 101 20.8544 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.064615 0.0040384 3.5204 7.955e-05 ***
## Residuals 85 0.097508 0.0011472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.091 1.09096 6.3952 0.06011 0.001 ***
## Residuals 100 17.059 0.17059 0.93989
## Total 101 18.150 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# nestedness
fung1<-fung[env$Treatment1=="Control"& env$TP>0,]
env1<-env[env$Treatment1=="Control"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Control" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Control" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jne
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[1:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[1:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.020609 0.0012881 1.0685 0.3973
## Residuals 85 0.102462 0.0012054
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.07362 0.073624 18.313 0.15478 0.001 ***
## Residuals 100 0.40203 0.004020 0.84522
## Total 101 0.47566 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.014805 0.00092531 1.2528 0.2471
## Residuals 85 0.062782 0.00073861
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.07138 -0.071378 -15.686 -0.18604 1
## Residuals 100 0.45505 0.004550 1.18604
## Total 101 0.38367 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.009969 0.00062307 0.9983 0.4667
## Residuals 85 0.053053 0.00062415
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.039555 -0.039555 -17.571 -0.21317 1
## Residuals 100 0.225109 0.002251 1.21317
## Total 101 0.185554 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 16 0.009838 0.00061490 1.5158 0.113
## Residuals 85 0.034481 0.00040566
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.00435 -0.0043503 -4.0087 -0.04176 1
## Residuals 100 0.10852 0.0010852 1.04176
## Total 101 0.10417 1.00000
#
# bray
fung1<-fung[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[3:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[3:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.68381 0.048844 18.586 < 2.2e-16 ***
## Residuals 75 0.19710 0.002628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 4.4096 4.4096 42.556 0.32596 0.001 ***
## Residuals 88 9.1184 0.1036 0.67404
## Total 89 13.5280 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.077845 0.0055604 4.5858 6.491e-06 ***
## Residuals 75 0.090939 0.0012125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.8371 2.83713 32.667 0.27072 0.001 ***
## Residuals 88 7.6428 0.08685 0.72928
## Total 89 10.4799 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.042857 0.0030612 1.9335 0.03574 *
## Residuals 75 0.118740 0.0015832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.8944 2.89439 27.361 0.23717 0.001 ***
## Residuals 88 9.3092 0.10579 0.76283
## Total 89 12.2036 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.039147 0.0027962 0.8791 0.5837
## Residuals 75 0.238556 0.0031807
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.6457 0.64567 6.232 0.06614 0.001 ***
## Residuals 88 9.1173 0.10361 0.93386
## Total 89 9.7629 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# jaccard
fung1<-fung[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"jaccard")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[3:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[3:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.76251 0.054465 23.286 < 2.2e-16 ***
## Residuals 75 0.17542 0.002339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 5.2063 5.2063 28.642 0.24556 0.001 ***
## Residuals 88 15.9957 0.1818 0.75444
## Total 89 21.2020 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.085245 0.0060889 4.7351 4.088e-06 ***
## Residuals 75 0.096444 0.0012859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.4643 3.4643 20.293 0.18739 0.001 ***
## Residuals 88 15.0229 0.1707 0.81261
## Total 89 18.4872 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.040054 0.0028610 1.9757 0.03128 *
## Residuals 75 0.108606 0.0014481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 3.2589 3.2589 16.562 0.1584 0.001 ***
## Residuals 88 17.3155 0.1968 0.8416
## Total 89 20.5744 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.029557 0.0021112 0.8911 0.5713
## Residuals 75 0.177685 0.0023691
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.9102 0.91022 4.7216 0.05092 0.001 ***
## Residuals 88 16.9646 0.19278 0.94908
## Total 89 17.8748 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# turnover
fung1<-fung[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[3:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[3:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.52369 0.037406 7.0397 5.392e-09 ***
## Residuals 75 0.39852 0.005314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.803 2.8030 17.378 0.16491 0.001 ***
## Residuals 88 14.194 0.1613 0.83509
## Total 89 16.997 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.079233 0.0056595 1.4347 0.1586
## Residuals 75 0.295865 0.0039449
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 2.9114 2.91143 19.937 0.18471 0.001 ***
## Residuals 88 12.8505 0.14603 0.81529
## Total 89 15.7620 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.035766 0.0025547 0.8698 0.5934
## Residuals 75 0.220277 0.0029370
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.9958 1.99575 12.108 0.12095 0.001 ***
## Residuals 88 14.5051 0.16483 0.87905
## Total 89 16.5009 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.036764 0.0026260 1.1822 0.3065
## Residuals 75 0.166592 0.0022212
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.6974 0.69742 4.3252 0.04685 0.001 ***
## Residuals 88 14.1897 0.16125 0.95315
## Total 89 14.8871 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# nestedness
fung1<-fung[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Pre_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Pre_flowering_drought" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jne
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[3:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[3:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.025169 0.00179778 1.8644 0.04437 *
## Residuals 75 0.072320 0.00096427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.16009 0.160091 30.765 0.25904 0.001 ***
## Residuals 88 0.45793 0.005204 0.74096
## Total 89 0.61802 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.041644 0.0029746 2.7577 0.002431 **
## Residuals 75 0.080899 0.0010787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.06717 -0.067167 -18.317 -0.26285 1
## Residuals 88 0.32270 0.003667 1.26285
## Total 89 0.25553 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.021875 0.00156247 1.6505 0.08517 .
## Residuals 75 0.070999 0.00094665
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.033275 -0.033275 -9.5087 -0.12114 1
## Residuals 88 0.307955 0.003499 1.12114
## Total 89 0.274679 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 14 0.0054412 0.00038866 0.9835 0.4783
## Residuals 75 0.0296389 0.00039519
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.003232 -0.0032316 -2.8542 -0.03352 1
## Residuals 88 0.099637 0.0011322 1.03352
## Total 89 0.096405 1.00000
##
# bray
fung1<-fung[env$Treatment1=="Post_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Post_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[8:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[8:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.17847 0.0198298 5.5192 2.942e-05 ***
## Residuals 50 0.17964 0.0035929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.7051 0.70512 13.38 0.18744 0.001 ***
## Residuals 58 3.0566 0.05270 0.81256
## Total 59 3.7618 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.015321 0.0017023 1.1565 0.3428
## Residuals 50 0.073594 0.0014719
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.3579 0.35788 6.2749 0.09763 0.001 ***
## Residuals 58 3.3079 0.05703 0.90237
## Total 59 3.6658 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.01174 0.0013044 1.1153 0.3697
## Residuals 50 0.05848 0.0011696
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.6827 0.68269 7.5284 0.11489 0.001 ***
## Residuals 58 5.2596 0.09068 0.88511
## Total 59 5.9423 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.01841 0.0020456 1.0032 0.45
## Residuals 50 0.10196 0.0020391
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.4090 0.40896 4.0263 0.06491 0.001 ***
## Residuals 58 5.8911 0.10157 0.93509
## Total 59 6.3001 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# jaccard
fung1<-fung[env$Treatment1=="Post_flowering_drought"& env$TP>0,]
env1<-env[env$Treatment1=="Post_flowering_drought"& env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
fung.dist<-vegdist(decostand(fungx,"hellinger"),"jaccard")
#fungx[fungx>0]=1
#fd<-beta.pair(fungx, index.family = "jaccard")
#fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[8:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[8:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.22856 0.0253957 5.2849 4.679e-05 ***
## Residuals 50 0.24027 0.0048053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 1.1540 1.15402 10.38 0.15179 0.001 ***
## Residuals 58 6.4486 0.11118 0.84821
## Total 59 7.6026 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.020118 0.0022353 1.1492 0.3475
## Residuals 50 0.097259 0.0019452
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.6251 0.62505 4.966 0.07887 0.001 ***
## Residuals 58 7.3002 0.12587 0.92113
## Total 59 7.9253 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.012154 0.0013504 1.1543 0.3442
## Residuals 50 0.058496 0.0011699
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.9378 0.93777 5.2882 0.08356 0.001 ***
## Residuals 58 10.2852 0.17733 0.91644
## Total 59 11.2229 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.016116 0.0017907 1.0205 0.4369
## Residuals 50 0.087734 0.0017547
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.5659 0.56591 2.9595 0.04855 0.001 ***
## Residuals 58 11.0905 0.19121 0.95145
## Total 59 11.6564 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# turnover
fung1<-fung[env$Treatment1=="Post_flowering_drought" & env$TP>0,]
env1<-env[env$Treatment1=="Post_flowering_drought" & env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jtu
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[8:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[8:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.25513 0.0283481 3.9941 0.0006833 ***
## Residuals 50 0.35487 0.0070974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.3048 -0.30479 -2.8554 -0.05178 1
## Residuals 58 6.1910 0.10674 1.05178
## Total 59 5.8862 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.048078 0.005342 1.1056 0.3762
## Residuals 50 0.241600 0.004832
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.5261 0.52605 4.0246 0.06489 0.001 ***
## Residuals 58 7.5812 0.13071 0.93511
## Total 59 8.1073 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.017583 0.0019537 0.9064 0.5271
## Residuals 50 0.107770 0.0021554
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.6085 0.60846 3.4969 0.05686 0.001 ***
## Residuals 58 10.0920 0.17400 0.94314
## Total 59 10.7004 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.005374 0.00059706 0.2732 0.9791
## Residuals 50 0.109267 0.00218533
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.3794 0.37940 2.3223 0.0385 0.001 ***
## Residuals 58 9.4756 0.16337 0.9615
## Total 59 9.8550 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# nestedness
fung1<-fung[env$Treatment1=="Post_flowering_drought" & env$TP>0,]
env1<-env[env$Treatment1=="Post_flowering_drought" & env$TP>0,]
par(mfrow=c(1,4),mar=c(2, 2, 0.5, 0.5))
for (i in c("Leaf","Root", "Rhizosphere", "Soil")){
fungx<-fung1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
envx<-env1[env1$Treatment1== "Post_flowering_drought" & env1$Habitat== i,]
#fung.dist<-vegdist(decostand(fungx,"hellinger"),"bray")
fungx[fungx>0]=1
fd<-beta.pair(fungx, index.family = "jaccard")
fung.dist<-fd$beta.jne
bd<-betadisper(fung.dist,factor(envx$TP))
print (anova(bd)); print(adonis(fung.dist~envx$TP))
plot(bd, col=blue2red(17)[8:17],
hull = FALSE,cex = 0.8,label.cex = 0.8,
seg.col=blue2red(17)[8:17], main="", xlab="PC1", ylab="PC2")
#boxplot(bd)
#print(TukeyHSD(bd))
}
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.06759 0.0075100 1.8822 0.07649 .
## Residuals 50 0.19950 0.0039901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 0.59236 0.59236 61.263 0.51368 0.001 ***
## Residuals 58 0.56081 0.00967 0.48632
## Total 59 1.15317 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.010894 0.0012104 1.1003 0.3798
## Residuals 50 0.055006 0.0011001
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.016041 -0.0160415 -3.0043 -0.05463 1
## Residuals 58 0.309694 0.0053396 1.05463
## Total 59 0.293653 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.005112 0.00056795 0.7762 0.6388
## Residuals 50 0.036584 0.00073168
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.000584 -0.00058388 -0.23495 -0.00407 0.797
## Residuals 58 0.144140 0.00248517 1.00407
## Total 59 0.143556 1.00000
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 9 0.015895 0.0017661 1.2365 0.2948
## Residuals 50 0.071412 0.0014282
##
## Call:
## adonis(formula = fung.dist ~ envx$TP)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## envx$TP 1 -0.004071 -0.0040710 -1.6924 -0.03006 0.983
## Residuals 58 0.139519 0.0024055 1.03006
## Total 59 0.135448 1.00000
